Case Discrimination: Self-supervised Feature Learning for the Classification of Focal Liver Lesions
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Several studies have shown performance improvement when constructing positive pairs with slices from the same CT series, images from the same imaging study, images from the same patient, and images from patients with similar age.
“In fact, several studies have shown performance improvement when constructing positive pairs with slices from the same CT series102, images from the same imaging study103, images from the same patient93 and images from patients with similar age62.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [102]
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
- Authors
- Dong, H., Iwamoto, Y., Han, X., Lin, L., Hu, H., Cai, X., & Chen, Y.-W.
- Affiliations
- Ritsumeikan University (JP)
- Zhejiang University (CN)
- Sir Run Run Shaw Hospital (CN)
- Corresponding author
- Lin, Lanfen
- Hu, Hongjie
- Chen, Yen-Wei
- Venue
- Smart Innovation, Systems and Technologies
- Publisher
- Springer Singapore
- Published
- 2021
- Pages
- 241-249
- ISSN
- 2190-3018 (ISSN-L)
- 2190-3026
- OpenAlex
- W3169025962
- Cited by
- 12
- References
- 21
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 3 · 2022: 1
- FWCI
- 3.76
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Smart innovation, systems and technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- focal liver lesions, transfer learning, ImageNet, medical image classification, fine-tuning, limited labeled data
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